IRANIAN JOURNAL OF ELECTRICAL AND ELECTRONIC ENGINEERING

en Deep Learning for Identification Malaria Diseases from Microscopic Image 4-Biomedical Signal & Image Processing Biomedical Signal & Image Processing Only For Articles of ELECRiS 2024 Only For Articles of ELECRiS 2024 <span style="font-size:10.0pt"><span new="" roman="" style="font-family:" times="">Malaria is a parasitic disease that causes significant morbidity and mortality worldwide. Early diagnosis and treatment are crucial for preventing complications and improving patient outcomes. Microscopic examination of blood smears remains the gold standard for malaria diagnosis, but it is time-consuming and requires skilled technicians. Deep learning has emerged as a promising tool for automated image analysis, including malaria diagnosis. In this study, we propose a novel approach for identifying malaria parasites in microscopic images using the GoogLeNet. Our method includes enhancement with the AGCS method, color transformation with grayscale, adaptive thresholding for segmentation, extraction, and GoogLeNet-based classification. We evaluated our method on a dataset of malaria blood smear images and achieved an accuracy of 95%, demonstrating the potential of GoogLeNet for automated malaria diagnosis.</span></span> Malaria diseases, deep learning, microscopic image, identification 116 123 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-5468-2&slc_lang=en&sid=1 Edy Victor Haryanto S edy@potensi-utama.ac.id 1800319475328460016493 1800319475328460016493 Yes Department of Engineering and Computer Science, Universitas Potensi Utama, Indonesia. Aimi Salihah Abdul Nasir aimisalihah@unimap.edu.my 1800319475328460016494 1800319475328460016494 No Department of Electrical Engineering and Technology, Universiti Malaysia Perlis, Malaysia. Mohd Yusoff Mashor yusoff@unimap.edu.my 1800319475328460016495 1800319475328460016495 No Department of Electrical Engineering and Technology, Universiti Malaysia Perlis, Malaysia. Bob Subhan Riza bob@potensi-utama.ac.id 1800319475328460016496 1800319475328460016496 No Department of Engineering and Computer Science, Universitas Potensi Utama, Indonesia. Zeehaida Mohamed zeehaida@usm.edu.my 1800319475328460016497 1800319475328460016497 No Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Malaysia.